Prediction of Chronic Diseases using Machine Learning Classifiers

Since there are no symptoms of chronic kidney disease (CKD) is a life-threatening state, and very difficult to diagnose early. A predictive model for estimating CKD is the aim of the proposed research, which aims to develop and validate this model. This problem most frequently affects adults and the elderly at this time. For preprocessing and cleaning, we will use the Decision Tree Classifier to input the data, and for analysis, we will use the Naive Bayes Classifier for the best prediction and accuracy. We used a clustering algorithm as a predictor; the clusters corresponded to the target categories; after that, we had a predictive model and various clusters to examine for each category. Then, we trained two different-sized neural nets and compared various supervised models by combining them with Principal Component Analysis.